Page 170 - Geochemical Anomaly and Mineral Prospectivity Mapping in GIS

Analysis of Geologic Controls on Mineral Occurrence 171

PC2 and PC3 scores derived in the application of exploratory data analysis or EDA
(Chapter 3) and shown in Figs. 3-20B and 3-20D, respectively; (2) the integrated PC3
and PC4 scores derived in the application of fractal analysis or FA (Chapter 4) and
shown in Fig. 4-21B; and (3) the integrated PC2 and PC3 scores derived in the
application of catchment basin analysis or CBA (Chapter 5) and shown in Fig. 5-12. The
set (1) of derivative SSGD is integrated first in the same way as the sets (2) and (3) of
derivative SSGD were integrated (see Chapters 4 and 5) so that these sets of derivative
SSGD can be compared properly with respect to the occurrences of epithermal Au
deposits in the case study area.
Fig. 6-12 shows the results of analyses of spatial association of the epithermal Au
deposits in the Aroroy district (Philippines) with the three sets of derivative SSGD. The
epithermal Au deposits have positive spatial associations with intermediate to high
values in each set of derivative SSGD. This is consistent with the fact that raw or
derivative SSGD represent transported materials whilst the epithermal Au deposits are in
situ so that highest values of the former are in many case located downstream and, thus,
not spatially coincident with the former. The positive spatial association between the
epithermal Au deposits and intermediate (to high) values in set (2) of derivative SSGD
(Figs. 6-12C and 6-12D) has the lowest statistical significance compared to the positive
spatial association between the epithermal Au deposits and intermediate (to high) values
in set (1) of derivative SSGD (Figs. 6-12A and 6-12B) and in set (3) of derivative SSGD
(Figs. 6-12E and 6-12F). However, considering that stream sediment anomalies
represent allochthonous materials whilst the epithermal Au deposits represent
autochthonous materials, using statistical significance as criterion to select which set of
derivative SSGD constitutes a set of optimal spatial evidence of mineral prospectivity
may not be appropriate. Alternatively, using the maximum value of β as reference, the
ratio of cumulative proportion of deposit pixels to cumulative proportion of sample
catchment basin (SCB) pixels, which represents conditional probability of deposit
occurrence given intermediate (to high) values of derivative SSGD, is a better criterion
to select which set of derivative SSGD constitutes a set of optimal spatial evidence of
mineral prospectivity. Thus, based on actual data used create the graphs in Fig. 6-12, set
(1) of derivative SSGD gives a ratio of 1.889 (i.e., 0.667÷0.353; data from Fig. 6-12A),
set (2) of derivative SSGD gives a ratio of 1.937 (i.e., 0.583÷0.301; data from Fig. 6-
12C) and set (3) of derivative SSGD gives a ratio of 1.962 (i.e., 0.667÷0.340; data from
Fig. 6-12E). Therefore, among the three sets of derivative SSGD, the integrated PC2 and
PC3 scores obtained from the CBA (Chapter 5) constitute the optimum spatial evidence
of epithermal Au prospectivity in the case study area, followed by the integrated PC3
and PC4 scores obtained from the FA (Chapter 4) and then the integrated PC2 and PC3
scores obtained from the EDA (Chapter 3). These results indicate that CBA presented in
this volume, which is actually a methodology (i.e., a collection of methods) rather than a
method, is improved when EDA and FA are incorporated in the methodology.
The preceding discussions demonstrate the usefulness of the distance distribution
method in determining spatial associations between occurrences of mineral deposits of
the type sought and various sets of geological features. However, it is important to take